111 citations as recorded by crossref.

1. Improvements and Operational Application of a Zero-Order Quick Assessment Model for Flood Damage: A Case Study in Italy L. Manselli et al. https://doi.org/10.3390/w14030373
2. Modelling flood damages under climate change conditions – a case study for Germany F. Hattermann et al. https://doi.org/10.5194/nhess-14-3151-2014
3. Flood damage functions based on a single physics- and data-based impact parameter that jointly accounts for water depth and velocity T. Lazzarin et al. https://doi.org/10.1016/j.jhydrol.2022.127485
4. A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. https://doi.org/10.1007/s11069-020-04413-x
5. Developing a multivariable lookup table function for estimating flood damages of rice crop in Vietnam using a secondary research approach N. Nguyen et al. https://doi.org/10.1016/j.ijdrr.2021.102208
6. A review of flood damage analysis for a building structure and contents M. Marvi https://doi.org/10.1007/s11069-020-03941-w
7. Resilience of art cities to flood risk: A quantitative model based on depth‐idleness correlation C. Arrighi et al. https://doi.org/10.1111/jfr3.12794
8. Flood impacts on individual well-being: insights from a post-event survey across differently exposed groups in Marche region, Italy S. Rrokaj et al. https://doi.org/10.1007/s11069-026-08045-5
9. Flood loss estimation using 3D city models and remote sensing data K. Schröter et al. https://doi.org/10.1016/j.envsoft.2018.03.032
10. Estimating changes in flood risks and benefits of non-structural adaptation strategies - a case study from Tyrol, Austria A. Thieken et al. https://doi.org/10.1007/s11027-014-9602-3
11. Extending coupled hydrological-hydraulic model chains with a surrogate model for the estimation of flood losses A. Zischg et al. https://doi.org/10.1016/j.envsoft.2018.08.009
12. Conceptual and Analytical Framework as Flood Risk Mapping Subsidy L. Batista & A. Ribeiro Neto https://doi.org/10.3390/geohazards3030020
13. The object-specific flood damage database HOWAS 21 P. Kellermann et al. https://doi.org/10.5194/nhess-20-2503-2020
14. Evaluation of residential building damage for the July 2021 flood in Westport, New Zealand R. Paulik et al. https://doi.org/10.1186/s40562-024-00323-z
15. Study on urban flood early warning system considering flood loss Y. Zang et al. https://doi.org/10.1016/j.ijdrr.2022.103042
16. Flood damage curves: new insights from the 2010 flood in Veneto, Italy A. Scorzini & E. Frank https://doi.org/10.1111/jfr3.12163
17. An Assessment of the Effectiveness of Tree-Based Models for Multi-Variate Flood Damage Assessment in Australia R. Hasanzadeh Nafari et al. https://doi.org/10.3390/w8070282
18. An Empirical, Functional approach to Depth Damages W. Lehman et al. https://doi.org/10.1051/e3sconf/20160705002
19. AGRIDE-c, a conceptual model for the estimation of flood damage to crops: development and implementation D. Molinari et al. https://doi.org/10.5194/nhess-19-2565-2019
20. How useful are complex flood damage models? K. Schröter et al. https://doi.org/10.1002/2013WR014396
21. Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties H. Lv et al. https://doi.org/10.1029/2021WR031552
22. Residential building and sub-building level flood damage analysis using simple and complex models R. Paulik et al. https://doi.org/10.1007/s11069-024-06756-1
23. Evolution of flood risk over large areas: Quantitative assessment for the Po river A. Domeneghetti et al. https://doi.org/10.1016/j.jhydrol.2015.05.043
24. Livable floodplain settlement, force adaptation, or landform adaptation? (Case in Bengawan Solo river downstream) B. Firsandy et al. https://doi.org/10.1088/1755-1315/780/1/012040
25. Existing evidence on the impact of climate risk on real estate valuations: a systematic map T. Piseddu & F. Vanhuyse https://doi.org/10.1186/s13750-026-00389-6
26. The RISPOSTA procedure for the collection, storage and analysis of high quality, consistent and reliable damage data in the aftermath of floods F. Ballio et al. https://doi.org/10.1111/jfr3.12216
27. Empirical flash flood vulnerability functions for residential buildings C. Arrighi et al. https://doi.org/10.1007/s42452-020-2696-1
28. Residential flood loss estimated from Bayesian multilevel models G. Mohor et al. https://doi.org/10.5194/nhess-21-1599-2021
29. Cost–benefit analysis of flood mitigation measures: a case study employing high-performance hydraulic and damage modelling D. Molinari et al. https://doi.org/10.1007/s11069-021-04814-6
30. Social disruption by flooding, a European perspective W. Ten Brinke et al. https://doi.org/10.1016/j.ijdrr.2017.01.011
31. Data Extraction Algorithm for Energy Performance Certificates (EPC) to Estimate the Maximum Economic Damage of Buildings for Economic Impact Assessment of Floods in Flanders, Belgium S. Van Ackere et al. https://doi.org/10.3390/ijgi7070272
32. Assessing the impact of climate change on flood inundation and agriculture in the Himalayan Mountainous Region of Bhutan P. Syldon et al. https://doi.org/10.1016/j.ejrh.2024.101687
33. Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. https://doi.org/10.5194/nhess-20-1149-2020
34. Short communication: A model to predict flood loss in mountain areas S. Fuchs et al. https://doi.org/10.1016/j.envsoft.2019.03.026
35. Supporting decision‐making on urban flood control alternatives through a recovery deficit procedure for successive events L. Guimarães & M. Miguez https://doi.org/10.1111/jfr3.12596
36. Has flood damage being reduced? A resident perspective on the effectiveness of flood management L. Hong et al. https://doi.org/10.1371/journal.pone.0325286
37. Challenges in paleoflood hydrology applied to risk analysis in mountainous watersheds – A review J. Bodoque et al. https://doi.org/10.1016/j.jhydrol.2014.12.004
38. Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models K. Vogel et al. https://doi.org/10.1029/2018WR022858
39. A GIS-based flood damage index for cultural heritage C. Arrighi et al. https://doi.org/10.1016/j.ijdrr.2023.103654
40. Estimating Financial Flood Damages in Residential and Commercial Regions: A Global Depth-Damage Approach S. Koohi et al. https://doi.org/10.1007/s11269-025-04390-5
41. Assessing coastal flood risk in a changing climate for the City of Vancouver T. Lyle & T. Mills https://doi.org/10.1080/07011784.2015.1126695
42. Assessment of Flood Losses with Household Responses: Agent-Based Simulation in an Urban Catchment Area L. Yang et al. https://doi.org/10.1007/s10666-018-9597-3
43. Evaluating targeted heuristics for vulnerability assessment in flood impact model chains A. Zischg et al. https://doi.org/10.1111/jfr3.12736
44. A Global Scale Analysis of River Flood Risk of UNESCO World Heritage Sites C. Arrighi https://doi.org/10.3389/frwa.2021.764459
45. From global circulation to local flood loss: Coupling models across the scales G. Felder et al. https://doi.org/10.1016/j.scitotenv.2018.04.170
46. Mapping direct flood impacts from a 2020 extreme flood event in Central Vietnam using spatial analysis techniques C. Luu et al. https://doi.org/10.1108/IJDRBE-07-2021-0070
47. Preface: Flood-risk analysis and integrated management P. Bubeck et al. https://doi.org/10.5194/nhess-16-1005-2016
48. Evaluating the cumulative costs of small-scale flash floods in Kuala Lumpur, Malaysia T. Bhuiyan et al. https://doi.org/10.1016/j.jhydrol.2022.128181
49. On the Influence of Input Data Quality to Flood Damage Estimation: The Performance of the INSYDE Model D. Molinari & A. Scorzini https://doi.org/10.3390/w9090688
50. Development of model for the estimation of direct flood damage including the movable property K. Zabret et al. https://doi.org/10.1111/jfr3.12255
51. Process-based flood damage modelling relying on expert knowledge: a methodological contribution applied to the agricultural sector P. Brémond et al. https://doi.org/10.5194/nhess-22-3385-2022
52. Effectiveness of flood forecasting and early warning system in the affected communities of Indus River Basin, Pakistan N. Jamal & A. Ur rahman https://doi.org/10.2166/wcc.2026.173
53. INSYDE-BE: adaptation of the INSYDE model to the Walloon region (Belgium) A. Scorzini et al. https://doi.org/10.5194/nhess-22-1743-2022
54. Flood vulnerability assessment of residential buildings by explicit damage process modelling R. Custer & K. Nishijima https://doi.org/10.1007/s11069-015-1725-7
55. Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures R. Hasanzadeh Nafari et al. https://doi.org/10.5194/nhess-17-1047-2017
56. Quantifying Flood Vulnerability Reduction via Private Precaution N. Sairam et al. https://doi.org/10.1029/2018EF000994
57. Risk-based flood protection planning under climate change and modeling uncertainty: a pre-alpine case study B. Dittes et al. https://doi.org/10.5194/nhess-18-1327-2018
58. Characterization of damages in buildings after floods in Vega Baja County (Spain) in 2019. The case study of Almoradí municipality R. Moya Barbera et al. https://doi.org/10.1016/j.cscm.2024.e03004
59. A comparison of building value models for flood risk analysis V. Röthlisberger et al. https://doi.org/10.5194/nhess-18-2431-2018
60. A Review of Flood Loss Models as Basis for Harmonization and Benchmarking T. Gerl et al. https://doi.org/10.1371/journal.pone.0159791
61. The determination of flood damage curve in areas lacking disaster data based on the optimization principle of variation coefficient and beta distribution Z. Wu et al. https://doi.org/10.1016/j.scitotenv.2020.142277
62. Uncertainty in flood damage estimates and its potential effect on investment decisions D. Wagenaar et al. https://doi.org/10.5194/nhess-16-1-2016
63. Urban pluvial flood risk assessment – data resolution and spatial scale when developing screening approaches on the microscale R. Löwe & K. Arnbjerg-Nielsen https://doi.org/10.5194/nhess-20-981-2020
64. Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. https://doi.org/10.5194/nhess-21-643-2021
65. Physical vulnerability to dynamic flooding: Vulnerability curves and vulnerability indices M. Papathoma-Köhle et al. https://doi.org/10.1016/j.jhydrol.2022.127501
66. Flood depth-damage and fragility functions derived with structured expert judgment G. Pita et al. https://doi.org/10.1016/j.jhydrol.2021.126982
67. Survey data of damaged residential buildings and business premises from the 2022 record-breaking flood in the Marche region, Italy S. Rrokaj et al. https://doi.org/10.5194/essd-17-6487-2025
68. Assessment of flood loss model transferability considering changes in precaution of flood-affected residents in Germany S. Kienzler et al. https://doi.org/10.1051/e3sconf/20160713002
69. Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. https://doi.org/10.5194/nhess-19-661-2019
70. INSYDE: a synthetic, probabilistic flood damage model based on explicit cost analysis F. Dottori et al. https://doi.org/10.5194/nhess-16-2577-2016
71. Validation of flood risk models: Current practice and possible improvements D. Molinari et al. https://doi.org/10.1016/j.ijdrr.2018.10.022
72. Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes N. Sairam et al. https://doi.org/10.1029/2019WR025068
73. Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. https://doi.org/10.1111/risa.13575
74. Ex post damage assessment: an Italian experience D. Molinari et al. https://doi.org/10.5194/nhess-14-901-2014
75. Development and evaluation of FLFAcs – A new Flood Loss Function for Australian commercial structures R. Hasanzadeh Nafari et al. https://doi.org/10.1016/j.ijdrr.2016.03.007
76. Development of generalized loss functions for rapid estimation of flood damages: a case study in Kelani River basin, Sri Lanka A. Komolafe et al. https://doi.org/10.1007/s12518-017-0200-4
77. Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts R. Paulik et al. https://doi.org/10.3390/jmse11101960
78. Multi-model ensembles for assessment of flood losses and associated uncertainty R. Figueiredo et al. https://doi.org/10.5194/nhess-18-1297-2018
79. Boosting vs. traditional machine learning models for flood susceptibility mapping: insights from a case study in central Vietnam N. Quang et al. https://doi.org/10.1016/j.asr.2025.07.105
80. Mapping social vulnerability to floods. A comprehensive framework using a vulnerability index approach and PCA analysis I. Ajtai et al. https://doi.org/10.1016/j.ecolind.2023.110838
81. Implementing the European “Floods Directive”: the Case of the Po River Basin D. Molinari et al. https://doi.org/10.1007/s11269-016-1248-3
82. Temporal development of flood risk considering settlement dynamics and local flood protection measures on catchment scale: an Austrian case study S. Achleitner et al. https://doi.org/10.1080/15715124.2016.1167061
83. A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. https://doi.org/10.1029/2019WR026213
84. A GIS-based tool for flood damage assessment and delineation of a methodology for future risk assessment: case study for Annotto Bay, Jamaica H. Glas et al. https://doi.org/10.1007/s11069-017-2920-5
85. A GIS-based procedure for preliminary mapping of pluvial flood risk at metropolitan scale C. Di Salvo et al. https://doi.org/10.1016/j.envsoft.2018.05.020
86. An introduction to flood modeling for catastrophe risk management S. Zanardo & J. Salinas https://doi.org/10.1002/wat2.1568
87. Calibration and validation of FLFArs -- a new flood loss function for Australian residential structures R. Hasanzadeh Nafari et al. https://doi.org/10.5194/nhess-16-15-2016
88. Capturing Regional Differences in Flood Vulnerability Improves Flood Loss Estimation N. Sairam et al. https://doi.org/10.3389/frwa.2022.817625
89. FLIAT, An Object-Relational GIS Tool for Flood Impact Assessment in Flanders, Belgium S. Van Ackere et al. https://doi.org/10.3390/w11040711
90. A Citizen-Contributed GIS Approach for Evaluating the Impacts of Land Use on Hurricane-Harvey-Induced Flooding in Houston Area D. Yang et al. https://doi.org/10.3390/land8020025
91. The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0 M. Di Bacco et al. https://doi.org/10.5194/nhess-24-1681-2024
92. Flood Depth‒Damage Curves for Spanish Urban Areas E. Martínez-Gomariz et al. https://doi.org/10.3390/su12072666
93. Tracing the value of data for flood loss modelling K. Schröter et al. https://doi.org/10.1051/e3sconf/20160705005
94. Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy) F. Carisi et al. https://doi.org/10.5194/nhess-18-2057-2018
95. The construction of flood loss ratio function in cities lacking loss data based on dynamic proportional substitution and hierarchical Bayesian model H. Lv et al. https://doi.org/10.1016/j.jhydrol.2020.125797
96. Sensitivity of flood loss estimates to building representation and flow depth attribution methods in micro-scale flood modelling M. Bermúdez & A. Zischg https://doi.org/10.1007/s11069-018-3270-7
97. Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach B. Winter et al. https://doi.org/10.5194/nhess-20-1689-2020
98. Measuring water level in rivers and lakes from lightweight Unmanned Aerial Vehicles F. Bandini et al. https://doi.org/10.1016/j.jhydrol.2017.02.038
99. Predictive applications of Australian flood loss models after a temporal and spatial transfer R. Hasanzadeh Nafari & T. Ngo https://doi.org/10.1080/19475705.2018.1445666
100. Econometric modelling for estimating direct flood damage to firms: a micro-scale approach using post-event records in Italy M. Ballocci et al. https://doi.org/10.5194/nhess-26-251-2026
101. A Robust and Transferable Model for the Prediction of Flood Losses on Household Contents M. Mosimann et al. https://doi.org/10.3390/w10111596
102. Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. https://doi.org/10.5194/nhess-20-2997-2020
103. A Zero-Order Flood Damage Model for Regional-Scale Quick Assessments A. Pogliani et al. https://doi.org/10.3390/w13091292
104. Towards global sensitivity analysis of large-scale flood loss models F. Pianosi et al. https://doi.org/10.5194/nhess-26-1727-2026
105. Sources of uncertainty in a probabilistic flood risk model B. Winter et al. https://doi.org/10.1007/s11069-017-3135-5
106. Assessing Economic Loss from Urban Waterlogging in Beijing under Climate Change Using a Hydraulic Model J. Zou et al. https://doi.org/10.1021/acssuschemeng.4c01707
107. Assessing vulnerability of buildings to hydro-meteorological hazards using an expert based approach – An application in Nehoiu Valley, Romania A. Godfrey et al. https://doi.org/10.1016/j.ijdrr.2015.06.001
108. Accounting public and individual flood protection measures in damage assessment: A novel approach for quantitative assessment of vulnerability and flood risk associated with local engineering adaptation options B. Rehan https://doi.org/10.1016/j.jhydrol.2018.06.061
109. Construction of flood loss function for cities lacking disaster data based on three-dimensional (object-function-array) data processing H. Lv et al. https://doi.org/10.1016/j.scitotenv.2021.145649
110. Regional and Temporal Transferability of Multivariable Flood Damage Models D. Wagenaar et al. https://doi.org/10.1029/2017WR022233
111. A Comparison of Factors Driving Flood Losses in Households Affected by Different Flood Types G. Mohor et al. https://doi.org/10.1029/2019WR025943